Data statistical method and related equipment

Abstract

The invention relates to the technical field of data statistics, in particular to a data statistics method and related equipment, and the method comprises the steps: obtaining to-be-counted data; analyzing the to-be-counted data, and generating a corresponding data label; judging whether the data label is a single label or not; if the data label is a single label, generating a statistical scheme based on the data label; if the data tags are not single tags, comprehensive tags are generated based on different data tags; generating a statistical scheme based on the comprehensive label; and generating a statistical result based on the statistical scheme. According to the method and the device, the accuracy of data statistics can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of data statistics, and in particular to a data statistics method and related equipment. Background Art

[0002] In today's digital age, the amount of data is growing explosively. Whether it is the operational data of enterprises, the experimental data of scientific research institutions, or the statistical data of government departments, effective statistics and analysis are required.

[0003] Traditional data statistics methods often have problems such as low efficiency and insufficient accuracy, and are difficult to meet the statistical needs of large-scale and complex data. With the rapid development of cloud computing and artificial intelligence, the efficiency of data statistics has been greatly improved. However, due to the inherent characteristics of different data or the diversity of data content, the data to be counted may be counted in different categories at the same time, resulting in data duplication and errors, and ultimately leading to low accuracy of data statistics. Summary of the invention

[0004] In order to help improve the accuracy of data statistics, the present application provides a data statistics method and related equipment.

[0005] In the first aspect, the present application provides a data statistics method, which adopts the following technical solution: A data statistics method, comprising: Get the pending statistical data; Analyze the statistical data to be counted and generate corresponding data labels; Determining whether the data label is a single label; If the data label is the single label, generating a statistical scheme based on the data label; If the data label is not the single label, generating a comprehensive label based on different data labels; Based on the comprehensive tags, generating a statistical scheme; Based on the statistical scheme, statistical results are generated.

[0006] By adopting the above technical solution, the data to be counted is first obtained, and the corresponding data label is generated after the data to be counted is analyzed. If the data label is a single label, it means that the data to be counted has only a unique corresponding data label, so the statistical plan is directly generated according to the unique corresponding data label; if the data label is not a single label, it means that the data to be counted has multiple data labels corresponding to it at the same time. In order to prevent the data to be counted into different categories according to multiple data labels at the same time, resulting in data duplication, it is necessary to generate a comprehensive label that contains all data label characteristics and is unique according to the multiple data labels corresponding to the data to be counted, and then generate a statistical plan according to the comprehensive label, and finally generate the corresponding statistical results according to the statistical plan; When there are multiple corresponding data labels for the statistical data, the characteristics of all the data labels are combined to generate a comprehensive label that can characterize all the data labels. Data statistics are then performed based on the comprehensive label. This helps reduce the occurrence of repeated statistics of the statistical data, thereby helping to improve the accuracy of data statistics.

[0007] Optionally, analyzing the to-be-stated data and generating corresponding data labels includes: Analyze the data to be counted, and obtain target keywords corresponding to the data to be counted; Obtaining the keyword quantity of the target keyword; Determine whether the number of keywords is equal to 1; If the number of keywords is equal to 1, a data tag is generated based on the target keyword.

[0008] Optionally, the analyzing the statistical data to be counted and obtaining the target keyword corresponding to the statistical data to be counted includes: Acquire the data properties to be counted; If the data is text data, then obtaining the text content of the text data; Obtaining a text title and a main idea based on the text content; Based on the text title and the central idea, obtaining target keywords; If the data is image data, obtaining a target object in the image data; Determine whether the target object has relevance; If the target object has a correlation, the target keyword is generated based on the target object and the correlation.

[0009] Optionally, after determining whether the number of keywords is equal to 1, the method further includes: If the number of the keywords is greater than 1, the to-be-stated data is analyzed, and the matching degree between the different target keywords and the to-be-stated data is obtained; Determine whether the matching degree exceeds a first degree threshold; If there is a matching degree that exceeds the first degree threshold, marking the corresponding matching degree as a target matching degree; Based on the target matching degree, a data label is generated.

[0010] Optionally, if the data label is not the single label, generating a comprehensive label based on different data labels includes: If the data tag is not the single tag, determining whether the data tag can be spliced; If the data tag can be spliced, generating a spliced ​​tag based on the data tag; Obtaining the splicing completion degree of different splicing tags; If the splicing completion degree meets the completion degree requirement, the splicing label is used as the comprehensive label; If the data tags cannot be spliced, determine whether there is a commonality between the data tags; If the label commonality exists between the data labels, the comprehensive label is generated based on the label commonality.

[0011] Optionally, if the data tags cannot be concatenated, determining whether there is a tag commonality between the data tags includes: If the data tags cannot be spliced, determining whether there is a containment relationship between different data tags; If there is a containment relationship between different data tags, it is determined that there is label commonality between the data tags; If there is no inclusion relationship between the different data tags, determining whether there is common superordinate information between the different data tags; If there is common superordinate information between different data tags, it is determined that there is tag commonality between the data tags.

[0012] Optionally, if the data tags cannot be concatenated, after determining whether there is a tag commonality between the data tags, the method further includes: If the data labels do not have the label commonality, the data labels are analyzed to determine whether there are erroneous labels; If the error label exists, regenerate the data label; If the error label does not exist, determining whether the data label is an original label; If the data tag is not the original tag, obtaining the number of associations corresponding to the data tag; Based on the number of associations, a target data tag is obtained, and the target data tag is used as the comprehensive tag.

[0013] In the second aspect, the present application also discloses a data statistics method system, which adopts the following technical solution: A data statistics method system, comprising: An acquisition module is used to obtain the statistical data to be counted; A first generating module is used to analyze the statistical data to be counted and generate corresponding data labels; A judging module, used to judge whether the data tag is a single tag; a second generating module, wherein if the data label is the single label, the second generating module is used to generate a statistical scheme based on the data label; A third generating module, if the data label is not the single label, the third generating module is used to generate a comprehensive label based on different data labels; A fourth generating module, used for generating a statistical scheme based on the comprehensive label; The fifth generating module is used to generate statistical results based on the statistical scheme.

[0014] By adopting the above technical solution, the data to be counted is first obtained, and the corresponding data label is generated after the data to be counted is analyzed. If the data label is a single label, it means that the data to be counted has only a unique corresponding data label, so the statistical plan is directly generated according to the unique corresponding data label; if the data label is not a single label, it means that the data to be counted has multiple data labels corresponding to it at the same time. In order to prevent the data to be counted into different categories according to multiple data labels at the same time, resulting in data duplication, it is necessary to generate a comprehensive label that contains all data label characteristics and is unique according to the multiple data labels corresponding to the data to be counted, and then generate a statistical plan according to the comprehensive label, and finally generate the corresponding statistical results according to the statistical plan; When there are multiple corresponding data labels for the statistical data, the characteristics of all the data labels are combined to generate a comprehensive label that can characterize all the data labels. Then, data statistics are performed based on the comprehensive label. This helps reduce the occurrence of repeated statistics of the statistical data, thereby helping to improve the accuracy of data statistics.

[0015] In a third aspect, the present application provides a computer device, which adopts the following technical solution: An intelligent terminal comprises a memory and a processor, wherein the memory is used to store a computer program that can be run on the processor, and when the processor loads the computer program, the method of the first aspect is executed.

[0016] By adopting the above technical solution, a computer program is generated based on the method of the first aspect and stored in a memory to be loaded and executed by a processor, thereby manufacturing an intelligent terminal based on the memory and the processor for user convenience.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program, and when the computer program is loaded by a processor, the method of the first aspect is executed.

[0018] By adopting the above technical solution, a computer program is generated based on the method of the first aspect and stored in a computer-readable storage medium so as to be loaded and executed by a processor. The computer-readable storage medium facilitates the reading and storage of the computer program.

[0019] In summary, this application includes the following beneficial technical effects: When there are multiple corresponding data labels for the statistical data, the characteristics of all the data labels are combined to generate a comprehensive label that can characterize all the data labels. Data statistics are then performed based on the comprehensive label. This helps reduce the occurrence of repeated statistics of the statistical data, thereby helping to improve the accuracy of data statistics. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a main flow chart of a data statistics method according to an embodiment of the present application; Figure 2 is a flowchart of steps S201 to S204; Figure 3 is a flowchart of steps S301 to S307; Figure 4 is a flowchart of steps S401 to S404; Figure 5 is a flowchart of steps S501 to S506; Figure 6 is a flowchart of steps S601 to S604; Figure 7 is a flowchart of steps S701 to S705; Figure 8 It is a module diagram of a data statistics method system in an embodiment of the present application.

[0021] Description of reference numerals: 1. Acquisition module; 2. First generation module; 3. Judgment module; 4. Second generation module; 5. Third generation module; 6. Fourth generation module; 7. Fifth generation module. DETAILED DESCRIPTION

[0022] In a first aspect, the present application discloses a data statistics method.

[0023] Reference Figure 1 , a data statistics method, comprising steps S101 to S107: Step S101: Obtain the statistical data to be counted.

[0024] Specifically, the data to be counted are data that need to be counted or are about to be counted. The data to be counted may be text data, image data, audio data, or other data. In this embodiment, the data to be counted are pre-stored in a data statistics system, and when they need to be counted, the data to be counted can be directly called.

[0025] Step S102: Analyze the statistical data and generate corresponding data labels.

[0026] Specifically, the data label is a label used to characterize the statistical data to be counted. In this embodiment, the statistical data to be counted can be analyzed to extract relevant features of the statistical data to be counted, and the corresponding data label can be generated according to the relevant features.

[0027] Step S103: Determine whether the data tag is a single tag.

[0028] Specifically, in this embodiment, a single tag refers to a single number of tags, that is, there is only one data tag.

[0029] Step S104: If the data label is a single label, a statistical scheme is generated based on the data label.

[0030] Specifically, if the data label is a single label, it means that the data to be counted has only one data label corresponding to it. Therefore, the data to be counted can be classified and counted directly according to the data label. In this embodiment, the statistical plan is the final plan for counting the data to be counted, such as which category of folder or database to put the data to be counted into.

[0031] Step S105: If the data label is not a single label, a comprehensive label is generated based on different data labels.

[0032] Specifically, if the data label is not a single label, it means that there are multiple corresponding data labels for the statistical data. In order to improve the accuracy of data statistics, all data labels need to be combined to generate a comprehensive label that can more completely characterize the statistical data. In this embodiment, the comprehensive label is a label that combines multiple data labels and can characterize the statistical data.

[0033] Step S106: Generate a statistical plan based on the comprehensive tags.

[0034] Specifically, in this embodiment, after the comprehensive label is generated, the data to be counted are classified and counted according to the comprehensive label.

[0035] Step S107: Generate statistical results based on the statistical plan.

[0036] The data statistics method provided in this embodiment first obtains the data to be counted, and generates corresponding data labels after analyzing the data to be counted. If the data label is a single label, it means that the data to be counted has only a unique corresponding data label, so the statistical plan is directly generated according to the unique corresponding data label; if the data label is not a single label, it means that the data to be counted has multiple data labels at the same time. In order to prevent the data to be counted into different categories according to multiple data labels at the same time, resulting in data duplication, it is necessary to generate a comprehensive label that contains all data label characteristics and is unique according to the multiple data labels corresponding to the data to be counted, and then generate a statistical plan according to the comprehensive label, and finally generate the corresponding statistical result according to the statistical plan.

[0037] When there are multiple corresponding data labels for the statistical data, the characteristics of all the data labels are combined to generate a comprehensive label that can characterize all the data labels. Then, data statistics are performed based on the comprehensive label. This helps reduce the occurrence of repeated statistics of the statistical data, thereby helping to improve the accuracy of data statistics.

[0038] Reference Figure 2 In one implementation of this embodiment, step S102 analyzes the statistical data and generates corresponding data tags, including steps S201 to S204: Step S201: Analyze the statistical data to be counted, and obtain the target keywords corresponding to the statistical data.

[0039] Specifically, in this embodiment, the target keyword is a keyword used to represent the statistical data to be counted.

[0040] Step S202: Obtain the keyword quantity of the target keyword.

[0041] Specifically, in this embodiment, the number of keywords is the number of target keywords.

[0042] Step S203: Determine whether the number of keywords is equal to 1.

[0043] Step S204: If the number of keywords is equal to 1, a data tag is generated based on the target keyword.

[0044] Specifically, in this embodiment, if the number of keywords is equal to 1, a corresponding data tag can be directly generated according to the target keyword.

[0045] Reference Figure 3 In one implementation of this embodiment, step S201 analyzes the statistical data to be counted, and obtaining the target keyword corresponding to the statistical data includes steps S301 to S307: Step S301: Obtain the properties of the data to be counted.

[0046] Specifically, in this embodiment, the data properties are the data attributes of the statistical data to be counted, including text attributes, image attributes, audio attributes and other attributes.

[0047] Step S302: If the data property is text data, then the text content of the text data is obtained.

[0048] Specifically, the text content refers to all the content of the data in this article.

[0049] Step S303: Obtain the text title and main idea based on the text content.

[0050] Specifically, the text title includes the name of the text data and all representative large and small titles in the text; the central idea is the idea or content that the text data wants to express; in this embodiment, the entire text content can be identified, and then the central idea of ​​the text data can be extracted based on the text content, or the text data can be directly input into the relevant model to extract the central idea.

[0051] Step S304: Obtain target keywords based on the text title and main idea.

[0052] Specifically, in this embodiment, the target keywords used to characterize the text data are determined based on the text title and the main idea; it is worth noting that if it is audio data, the audio data can be converted into text data and then the target keywords can be obtained according to the text data method.

[0053] Step S305: If the data property is image data, then obtain the target object in the image data.

[0054] Specifically, in this embodiment, the target object, that is, the main object in the image data, can be determined according to the size ratio or number ratio of different objects in the image data. For example, if the size ratio of the object exceeds 20% of the entire image, or if the individual object is small and the corresponding size ratio is less than 20%, but the total size ratio of its group exceeds 50% of the entire image, then this type of object can be uniformly used as the target object. Very prominent content in the image data can also be used as the target object, for example, if only the color of a certain object in the entire image is inconsistent with other objects and is very conspicuous.

[0055] Step S306: Determine whether the target object has any relevance.

[0056] Specifically, when there are multiple target objects at the same time, it is further determined whether there is any correlation between the multiple objects. In this embodiment, the determination can be made based on the characteristics of the target objects, such as the attributes and color of the target objects.

[0057] Step S307: If the target object has relevance, a target keyword is generated based on the target object and the relevance.

[0058] Specifically, in this embodiment, for example, there are multiple target objects in the image data at the same time, such as apples, pears, and watermelons, so it is not difficult to conclude that the multiple target objects are all fruits, so it can be determined that there is a correlation between the multiple target objects. Therefore, the properties of the target objects themselves can be combined and the related parts can be used as target keywords, that is, fruit can be used as the target keyword.

[0059] Reference Figure 4 In one implementation of this embodiment, after determining whether the number of keywords is equal to 1 in step S203, steps S401 to S404 are also included: Step S401: If the number of keywords is greater than 1, the statistical data to be analyzed is analyzed, and the matching degree between different target keywords and the statistical data to be obtained.

[0060] Specifically, in this embodiment, the degree of matching is obtained in different ways depending on the nature of the data to be counted. For example, if it is text data, the degree of matching can be determined based on the length and importance of the text data. If it is image data, the degree of matching can be determined based on the size ratio of the target object corresponding to the target keyword.

[0061] Step S402: Determine whether there is a matching degree exceeding a first degree threshold.

[0062] Specifically, in this embodiment, the first degree threshold is a pre-set judgment standard for judging whether a data label can be generated according to the matching degree.

[0063] Step S403: If there is a matching degree exceeding the first degree threshold, the corresponding matching degree is marked as a target matching degree.

[0064] Specifically, in this embodiment, the target matching degree is a matching degree that exceeds a first degree threshold.

[0065] Step S404: Generate data labels based on the target matching degree.

[0066] Specifically, after determining the target matching degree, a corresponding data tag is generated according to the target keyword corresponding to the target matching degree. In this embodiment, there may be multiple target matching degrees, so there may also be multiple corresponding data tags.

[0067] Reference Figure 5 In one implementation of this embodiment, step S105: if the data tag is not a single tag, generating a comprehensive tag based on different data tags includes steps S501 to S506: Step S501: If the data label is not a single label, determine whether the data label can be concatenated.

[0068] Specifically, in this embodiment, different labels can be simulated and spliced ​​one by one. If the label generated after splicing does not conform to the conventional naming rules or distorts the meaning of a single label, it means that the splicing cannot be performed. For example, if a watch and a watermelon are spliced ​​together, if it is "watch watermelon", it does not conform to the conventional naming rules. If it is "watermelon watch", although it has a clear definition, the meaning represented after splicing is inconsistent with the meaning to be represented by a single data label. Therefore, it is determined that the data labels cannot be spliced. The conventional naming rules are the conventional naming rules formed according to semantics and grammar in daily life.

[0069] Step S502: If the data tags can be spliced, a spliced ​​tag is generated based on the data tags.

[0070] Specifically, in this embodiment, the spliced ​​label is a label formed by splicing multiple data labels. For example, if the data labels are red and clothes, the spliced ​​label is red clothes.

[0071] Step S503: Obtain the splicing completion degree of different splicing labels.

[0072] Specifically, in this embodiment, the splicing completion is calculated based on the proportion of the number of data tags contained in the splicing label to the total number of data tags. For example, there are three data tags in total, namely white, Xiaomi and mobile phone. After splicing them, a splicing label white Xiaomi mobile phone can be generated. It is not difficult to see that the splicing label contains all three data tags, so the number of data tags contained in the direct label accounts for 100% of the total number of data tags, so the splicing completion is 100%.

[0073] Step S504: If the splicing completion degree meets the completion degree requirement, the splicing label is used as a comprehensive label.

[0074] Specifically, the completion requirement is a pre-set criterion for determining whether a comprehensive label can be generated according to the splicing completion. In this embodiment, the completion requirement is that the splicing completion reaches 100%.

[0075] Step S505: If the data tags cannot be spliced, determine whether there is any commonality between the data tags.

[0076] Specifically, in this embodiment, the label commonality refers to the common characteristics between different data labels, including data, semantics, and data.

[0077] Step S506: If there are label commonalities between the data labels, a comprehensive label is generated based on the label commonalities.

[0078] Reference Figure 6 In one implementation of this embodiment, if the data tags cannot be spliced ​​in step S505, determining whether there is a tag commonality between the data tags includes steps S601 to S604: Step S601: If the data tags cannot be concatenated, determine whether there is a containment relationship between different data tags.

[0079] Specifically, in this embodiment, the inclusion relationship is a relationship in which one data tag includes another data tag, for example, apple and red apple, red apple includes apple (from a literal perspective), and apple includes red apple (from the perspective of apple's properties and color).

[0080] Step S602: If there is a containment relationship between different data tags, it is determined that there is a tag commonality between the data tags.

[0081] Step S603: If there is no inclusion relationship between different data tags, it is determined whether there is common superordinate information between different data tags.

[0082] Specifically, in this embodiment, the common superordinate information refers to the common upper-level classification relationship between different label data. For example, apples and pears are both fruits, so their common superordinate information is fruits.

[0083] Step S604: If there is common superordinate information between different data tags, it is determined that there is tag commonality between the data tags.

[0084] Reference Figure 7 In one implementation of this embodiment, if the data tags cannot be spliced ​​in step S505, it is determined whether there is a commonality between the data tags and further includes steps S701 to S705: Step S701: If there is no label commonality between the data labels, the data labels are analyzed to determine whether there are erroneous labels.

[0085] Specifically, in this embodiment, an erroneous label is a label in which an error occurs.

[0086] Step S702: If there is an error label, regenerate the data label.

[0087] Specifically, in this embodiment, if it exists, it means that an error occurred when the data label was generated due to individual reasons, and therefore the data label to be counted needs to be regenerated.

[0088] Step S703: If there is no erroneous label, determine whether the data label is an original label.

[0089] Specifically, in this embodiment, the original label is a data label that has not undergone label fusion and superposition. For example, after apples and pears are superposed to form a new data label fruit, fruit is no longer the original label, but apples and pears are the original labels.

[0090] Step S704: If the data tag is not an original tag, the association count of the corresponding data tag is obtained.

[0091] Specifically, in this embodiment, the number of associations is the number of operations such as data label fusion and superposition. For example, if the original labels are durian candy, banana candy, and cake, then when generating a comprehensive label, the three data labels will first superposition durian candy and banana candy to fruit candy, and then superposition fruit candy and cake to sweets. A total of two superpositions are performed, so the number of associations is 2. Step S705: Based on the number of associations, obtain the target data tag and use the target data tag as a comprehensive tag.

[0092] Specifically, in this embodiment, the data tag with the most association times is selected as the comprehensive tag In a second aspect, the present application also discloses a data statistics method system.

[0093] Reference Figure 8 , a data statistics method system, comprising: An acquisition module is used to obtain the statistical data to be counted; The first generation module is used to analyze the statistical data and generate corresponding data labels; A judgment module, used to judge whether the data label is a single label; A second generation module, if the data label is a single label, the second generation module is used to generate a statistical scheme based on the data label; A third generation module, if the data label is not a single label, the third generation module is used to generate a comprehensive label based on different data labels; The fourth generation module is used to generate a statistical scheme based on the comprehensive labels; The fifth generating module is used to generate statistical results based on the statistical scheme.

[0094] In a third aspect, an embodiment of the present application discloses an intelligent terminal, including a memory and a processor, wherein the memory is used to store a computer program that can be run on the processor, and when the processor loads the computer program, a data statistics method of the above embodiment is executed.

[0095] In a fourth aspect, an embodiment of the present application discloses a computer-readable storage medium, and a computer program is stored in the computer-readable storage medium, wherein when the computer program is loaded by a processor, a data statistics method of the above embodiment is executed.

[0096] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A data statistics method, characterized in that: include: Get the pending statistical data; Analyze the statistical data to be counted and generate corresponding data labels; Determining whether the data label is a single label; If the data label is the single label, generating a statistical scheme based on the data label; If the data label is not the single label, generating a comprehensive label based on different data labels; Based on the comprehensive tags, generating a statistical scheme; Based on the statistical scheme, statistical results are generated.

2. A data statistics method according to claim 1, characterized in that: The analyzing the to-be-stated data and generating corresponding data labels comprises: Analyze the data to be counted, and obtain target keywords corresponding to the data to be counted; Obtaining the keyword quantity of the target keyword; Determine whether the number of keywords is equal to 1; If the number of keywords is equal to 1, a data tag is generated based on the target keyword.

3. A data statistics method according to claim 2, characterized in that: The analyzing the statistics to be counted and obtaining the target keywords corresponding to the statistics to be counted includes: Acquire the data properties to be counted; If the data is text data, then obtaining the text content of the text data; Obtaining a text title and a main idea based on the text content; Based on the text title and the central idea, obtaining target keywords; If the data is image data, obtaining a target object in the image data; Determine whether the target object has relevance; If the target object has a correlation, the target keyword is generated based on the target object and the correlation.

4. A data statistics method according to claim 2, characterized in that: After determining whether the number of keywords is equal to 1, the method further includes: If the number of the keywords is greater than 1, the to-be-stated data is analyzed, and the matching degree between the different target keywords and the to-be-stated data is obtained; Determine whether the matching degree exceeds a first degree threshold; If there is a matching degree that exceeds the first degree threshold, marking the corresponding matching degree as a target matching degree; Based on the target matching degree, a data label is generated.

5. A data statistics method according to claim 1, characterized in that: If the data label is not the single label, generating a comprehensive label based on different data labels includes: If the data tag is not the single tag, determining whether the data tag can be spliced; If the data tag can be spliced, generating a spliced ​​tag based on the data tag; Obtaining the splicing completion degree of different splicing tags; If the splicing completion degree meets the completion degree requirement, the splicing label is used as the comprehensive label; If the data tags cannot be spliced, determine whether there is a commonality between the data tags; If the label commonality exists between the data labels, the comprehensive label is generated based on the label commonality.

6. A data statistics method according to claim 5, characterized in that: If the data tags cannot be spliced, determining whether there is a tag commonality between the data tags includes: If the data tags cannot be spliced, determining whether there is a containment relationship between different data tags; If there is a containment relationship between different data tags, it is determined that there is label commonality between the data tags; If there is no inclusion relationship between the different data tags, determining whether there is common superordinate information between the different data tags; If there is common superordinate information between different data tags, it is determined that there is tag commonality between the data tags.

7. A data statistics method according to claim 5, characterized in that: If the data tags cannot be concatenated, after determining whether there is a tag commonality between the data tags, the method further includes: If the data labels do not have the label commonality, the data labels are analyzed to determine whether there are erroneous labels; If the error label exists, regenerate the data label; If the error label does not exist, determining whether the data label is an original label; If the data tag is not the original tag, obtaining the number of associations corresponding to the data tag; Based on the number of associations, a target data tag is obtained, and the target data tag is used as the comprehensive tag.

8. A data statistics method system, characterized in that: include: An acquisition module is used to obtain the statistical data to be counted; A first generating module is used to analyze the statistical data to be counted and generate corresponding data labels; A judging module, used to judge whether the data tag is a single tag; a second generating module, wherein if the data label is the single label, the second generating module is used to generate a statistical scheme based on the data label; A third generating module, if the data label is not the single label, the third generating module is used to generate a comprehensive label based on different data labels; A fourth generating module, used for generating a statistical scheme based on the comprehensive label; The fifth generating module is used to generate statistical results based on the statistical scheme.

9. An intelligent terminal, comprising a memory and a processor, characterized in that: The memory is used to store a computer program that can be run on the processor, and when the processor loads the computer program, it executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored therein, characterized in that: When the computer program is loaded by a processor, the method according to any one of claims 1 to 7 is executed.

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